Optimum design of reinforced concrete continuous beam and slab systems using genetic algorithms

Abstract

Abstract Producing a structural design that fulfills strength, serviceability, and design code requirements with the least cost is the goal of every engineer. This paper aims to apply genetic algorithms to achieve the optimum design of reinforced concrete floors consisting of slabs and beams. The floor system consists of continuous main beams with three spans and variable locations of internal supports and a secondary slab/beam system. The design variables include the main beam spacing, the main beam outer-to-inner span ratio, the main beam depth, and the type of the secondary system. The genetic algorithms using MATLAB’s implanted global optimization toolbox were applied in this study. The design respects all the requirements of ACI 318–19. The impact of the ratio between the cost of steel per ton and that of concrete per cubic meter, and the applied live load on the optimum solution has also been studied. The results of the present study showed that the optimum design was always achieved using the one-way slab secondary system with the maximum possible main beam spacing that kept the slab thickness and reinforcement at minimum values. For high live load values (8 and 10 kN/m 2 ) and low steel-to-concrete cost ratios (15–20), it was preferred to use main beams with equal spans. The optimum main beam depth lay between 1.3 and 1.56 times the minimum depth required for the moment.